Evaluation of a proteomic signature coupled with the kidney failure risk equation in predicting end stage kidney disease in a chronic kidney disease cohort

Carlos Raúl Ramírez Medina; Ibrahim Ali; Ivona Baricevic-Jones; Moin A. Saleem; Anthony D. Whetton; Philip A. Kalra; Nophar Geifman

doi:10.1186/s12014-024-09486-5

Evaluation of a proteomic signature coupled with the kidney failure risk equation in predicting end stage kidney disease in a chronic kidney disease cohort

Carlos Raúl Ramírez Medina, Ibrahim Ali, Ivona Baricevic-Jones, Moin A. Saleem, Anthony D. Whetton, Philip A. Kalra, Nophar Geifman

Research output: Contribution to journal › Article › peer-review

Abstract

The early identification of patients at high-risk for end-stage renal disease (ESRD) is essential for providing optimal care and implementing targeted prevention strategies. While the Kidney Failure Risk Equation (KFRE) offers a more accurate prediction of ESRD risk compared to static eGFR-based thresholds, it does not provide insights into the patient-specific biological mechanisms that drive ESRD. This study focused on evaluating the effectiveness of KFRE in a UK-based advanced chronic kidney disease (CKD) cohort and investigating whether the integration of a proteomic signature could enhance 5-year ESRD prediction.

Original language	English
Journal	Clinical Proteomics
Volume	21
Issue number	1
DOIs	https://doi.org/10.1186/s12014-024-09486-5
Publication status	Published - 2024

Keywords

Machine Learning
Proteomics
chronic kidney disease
Random Forest
Boruta

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1186/s12014-024-09486-5

Cite this

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title = "Evaluation of a proteomic signature coupled with the kidney failure risk equation in predicting end stage kidney disease in a chronic kidney disease cohort",

abstract = "The early identification of patients at high-risk for end-stage renal disease (ESRD) is essential for providing optimal care and implementing targeted prevention strategies. While the Kidney Failure Risk Equation (KFRE) offers a more accurate prediction of ESRD risk compared to static eGFR-based thresholds, it does not provide insights into the patient-specific biological mechanisms that drive ESRD. This study focused on evaluating the effectiveness of KFRE in a UK-based advanced chronic kidney disease (CKD) cohort and investigating whether the integration of a proteomic signature could enhance 5-year ESRD prediction.",

keywords = "Machine Learning, Proteomics, chronic kidney disease, Random Forest, Boruta",

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T1 - Evaluation of a proteomic signature coupled with the kidney failure risk equation in predicting end stage kidney disease in a chronic kidney disease cohort

AU - Ramírez Medina, Carlos Raúl

AU - Ali, Ibrahim

AU - Baricevic-Jones, Ivona

AU - Saleem, Moin A.

AU - Whetton, Anthony D.

AU - Kalra, Philip A.

AU - Geifman, Nophar

PY - 2024

Y1 - 2024

N2 - The early identification of patients at high-risk for end-stage renal disease (ESRD) is essential for providing optimal care and implementing targeted prevention strategies. While the Kidney Failure Risk Equation (KFRE) offers a more accurate prediction of ESRD risk compared to static eGFR-based thresholds, it does not provide insights into the patient-specific biological mechanisms that drive ESRD. This study focused on evaluating the effectiveness of KFRE in a UK-based advanced chronic kidney disease (CKD) cohort and investigating whether the integration of a proteomic signature could enhance 5-year ESRD prediction.

AB - The early identification of patients at high-risk for end-stage renal disease (ESRD) is essential for providing optimal care and implementing targeted prevention strategies. While the Kidney Failure Risk Equation (KFRE) offers a more accurate prediction of ESRD risk compared to static eGFR-based thresholds, it does not provide insights into the patient-specific biological mechanisms that drive ESRD. This study focused on evaluating the effectiveness of KFRE in a UK-based advanced chronic kidney disease (CKD) cohort and investigating whether the integration of a proteomic signature could enhance 5-year ESRD prediction.

KW - Machine Learning

KW - Proteomics

KW - chronic kidney disease

KW - Random Forest

KW - Boruta

U2 - 10.1186/s12014-024-09486-5

DO - 10.1186/s12014-024-09486-5

M3 - Article

SN - 1559-0275

VL - 21

JO - Clinical Proteomics

JF - Clinical Proteomics

IS - 1

ER -

Evaluation of a proteomic signature coupled with the kidney failure risk equation in predicting end stage kidney disease in a chronic kidney disease cohort

Abstract

Keywords

UN SDGs

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